https://github.com/himank-khatri/classiflow
A web app that automates tedious data preprocessing and machine learning model testing.
https://github.com/himank-khatri/classiflow
exploratory-data-analysis machinelearning preprocessing-data python streamlit vizualization
Last synced: 3 months ago
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A web app that automates tedious data preprocessing and machine learning model testing.
- Host: GitHub
- URL: https://github.com/himank-khatri/classiflow
- Owner: Himank-Khatri
- License: mit
- Created: 2024-09-25T13:15:42.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-05T04:32:05.000Z (8 months ago)
- Last Synced: 2025-03-12T02:54:54.156Z (7 months ago)
- Topics: exploratory-data-analysis, machinelearning, preprocessing-data, python, streamlit, vizualization
- Language: Python
- Homepage: https://classification-builder.streamlit.app/
- Size: 258 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ClassiFlow
**ClassiFlow** is a user-friendly web application built with Streamlit that allows users to easily perform exploratory data analysis (EDA), preprocess datasets, train machine learning classification models, and evaluate them using various performance metrics and visualizations.
## Demo
Check out the live web app here: [ClassiFlow](https://classiflow.streamlit.app/)
## Features
- **Data Import**: Upload `.csv` or `.xlsx` datasets.
- **Preprocessing**: Handle missing values, encode categorical data, normalize, scale, and split datasets for training/testing.
- **EDA**: Visualize missing values, data distributions, and scatter plots.
- **Model Training**: Choose from Logistic Regression, Naive Bayes, SVM, KNN, Decision Tree, and Random Forest classifiers.
- **Evaluation**: Visualize performance metrics such as ROC curves, confusion matrices, and accuracy.
- **Model Comparison**: Add and compare multiple models.
## Installation
To run this app locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/Himank-Khatri/classiflow.git
```
2. Navigate to the project directory:
```bash
cd classiflow
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Streamlit app:
```bash
streamlit run app.py --server.enableXsrfProtection false
```
## Project Structure
- `app.py`: The main Streamlit application file.
- `utils/`: Contains the helper functions for preprocessing, model training, and visualization.
- `requirements.txt`: List of dependencies required to run the app.
## Contributing
Feel free to contribute to the project by creating issues or submitting pull requests on the GitHub repository: [GitHub Repository](https://github.com/Himank-Khatri/ClassiFlow/).
## License
This project is licensed under the MIT License.